Serum D-serine to total serine ratio and glycine levels as predictive biomarkers for cognitive dysfunction in frail elderly subjects

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Despite recent research efforts, the physiopathological mechanisms concurring to determine frailty remain elusive and biomarkers able to predate its occurrence in the early stages are still lacking. Beyond its physical component, cognitive decline represents a critical domain of frailty associated with higher risk of adverse health outcomes. We measured by High Performance Liquid Chromatography (HPLC) a pool of serum amino acids including L-glutamate, L-aspartate, glycine and D-serine, as well as their precursors L-glutamine, L-asparagine and L-serine in a cohort of elderly subjects encompassing the entire continuum from fitness to frailty. These amino acids are known to orchestrate excitatory and inhibitory neurotransmission, and in turn, to play a key role as intermediates of energy homeostasis and in liver, kidney, muscle and immune system metabolism. To comprehensively assess frailty, we employed both the Edmonton Frail Scale (EFS), as a practical tool to capture the multidimensionality of frailty, and the frailty phenotype, as a measure of physical function. We found that D-serine and D-/Total serine ratio were independent predictors of EFS but not of physical frailty. Furthermore, higher glycine levels and D-/Total serine correlated with worse cognition and depressive symptoms in the frail group. These findings suggest that altered homeostasis of D-serine may represent a biochemical signature of frailty, while increased serum glycine and D-/Total serine ratio could be specifically associated with cognitive decline and depression in frail older populations. *Alberto Imarisio and Isar Yahyavi share first authorship **Alessandro Usiello and Enza Maria Valente share senior authorship Health sciences/Biomarkers/Diagnostic markers Biological sciences/Neuroscience/Molecular neuroscience Biological sciences/Neuroscience/Learning and memory/Long-term memory Health sciences/Diseases/Psychiatric disorders/Depression Health sciences/Biomarkers/Prognostic markers Figures Figure 1 Figure 2 Figure 3 Introduction Frailty is a complex clinical syndrome characterized by a progressive deterioration of physiological function of multiple organ systems, with consequent increased vulnerability to stressors and adverse health outcomes 1 . Frailty is common in elderly populations, with a prevalence in high-income countries ranging from 4 to 16 percent in people over 65 years of age and featuring a two-fold higher risk in women than men 2–4 . Frailty is recognized as a main determinant of disability, institutionalization and mortality among older people. However, frailty also represents a dynamic condition which exists on a continuum from fit to frail, where a subject’s status can change in either direction over time 5 . Previous longitudinal studies showed indeed that up to 57% of individuals experience at least one transition, which includes both worsening and improvement in frailty state 4,6 . This evidence suggests that the factors concurring to determine frailty may be targeted with preventive interventions to reduce its burden on health outcomes. Heterogeneous frailty definitions and operational scales have been proposed, with large variations in their biological rationale and included components 7 . Among the most commonly adopted, the Fried’s frailty phenotype considers frailty as a biological syndrome and classifies individuals on the basis of five physical components 8 . A few years later, Rolfson and colleagues proposed the Edmonton Frail Scale (EFS), a brief and point-of-care frailty evaluation tool whose reliability is comparable to the most comprehensive geriatric assessment scales 9,10 . Among its nine items, the EFS includes an assessment of primary brain-related functions including cognition, mood and social support, whose impairment represents a key component of frailty and is associated with increased social isolation, disability and mortality 11,12 . Despite the physiopathological mechanisms responsible for frailty still remain elusive, frailty prevalence and incidence have been linked to several defective physiological processes, including altered insulin resistance, alterations in energy-regulatory hormones, impaired musculoskeletal system function and mitochondrial energy production, autonomic nervous system dysfunction and systemic inflammation 13 . Consistent with the complex nature of frailty syndrome, several alterations involving multiple pathways and cellular processes in distinct organs have been disclosed by OMICS approaches 14 . In particular, recent metabolomics studies described biochemical alterations in frail subjects, including variations in anti-oxidant, inflammation, purine, urea cycle, kidney markers, tricarboxylic acid cycle and amino acids pathways 15,16,25,17–24 . The discovery of reliable biomarkers of frailty represents therefore a key milestone for identifying and monitoring the course of this syndrome along aging and, in turn, offering a possible therapeutic approach aimed at reverting frailty. However, previous OMICS results are inconsistent among independent studies 14 and, except for pro-inflammatory soluble cytokines, which are commonly increased in older frail subjects 26 , a unified biochemical marker representative of this syndrome is currently lacking. Moreover, given the critical relevance of cognitive decline, and mood alterations reported in frailty 12,27,28 , the identification of a specific biochemical hallmark mirroring the progressive decay of brain functions before the occurrence of overt dementia represents an unmet clinical need. In light of this knowledge gap, here we measured by High Performance Liquid Chromatography (HPLC) a pool of amino acids that collectively are known to modulate glutamatergic receptors activation (L-glutamate, L-aspartate, glycine, D-serine) or to represent the immediate precursors of these neuroactive molecules (L-glutamine, L-asparagine and L-serine) in a well-characterized cohort of elderly subjects encompassing the entire continuum from non-frail to frail condition. Noteworthy, in addition to their neuroactive role, these amino acids play critical roles in regulating various cellular pathways, including protein synthesis, tricarboxylic acid cycle, redox homeostasis, ammonium recycling, purine nucleotide cycle, folate and methionine cycles, and the synthesis of sphingolipids and phospholipids 29 . Consistently with their vital relevance in orchestrating cognitive energy homeostasis and immune system functions, as well as the metabolism of various peripheral organs, such as muscles, liver and kidney, which are severely affected in frail subjects 1 , we investigated the relationship between the serum levels of these metabolites and frailty. We assessed frailty status with (i) the EFS score, which we adopted as a reliable instrument mirroring the multidimensionality of frailty 9 ; (ii) the Fried’s phenotype, as a well-established tool to evaluate the physical domain of frailty 8 . We also took into account the effect of several comorbidities and health parameters representing key components of frailty and potentially impacting the blood levels of amino acids, including body mass index (BMI), visceral adipose tissue (VAT), sarcopenia, diabetes mellitus and cigarette smoking. Methods Participants Enrolment and inclusion/exclusion criteria Forty-five consecutive hospitalized subjects were recruited at the Physical Medicine and Rehabilitation Unit of Istituto Santa Margherita, Pavia, Italy, between February 2019 and August 2021. Eighty additional outpatients were recruited at the Endocrinology and Nutrition Unit of the same institute. The patients were included if ( 1 ) admitted for functional loss secondary to a non-disabling disease; ( 2 ) aged 65 years or older. The following exclusion criteria were applied: 1) any disease that could directly affect muscle strength (including neurological diseases, hip fractures or amputations); 2) dementia according to DSM-5 criteria 30 ; 3) any systemic condition potentially affecting serum amino acid levels, including kidney, liver, rheumatologic and neoplastic diseases, history of drug or alcohol abuse; ( 4 ) history of altered serum creatinine levels (> 1.2 mg/dl) or liver function parameters (aspartate transaminase or alanine transaminase > 50 U/l). Smoking status (current/former/never smoker) was assessed trough interview. The total number of drugs habitually taken by subjects was retrieved from medical records. This study was approved by the local ethics committee (protocol 20180097520, 09/11/2018) and was in conformity with the Helsinki Declaration. Written informed consent was obtained from all participants. Cognitive and mood evaluation Each subject underwent a standardized examination including evaluation of global cognition, performed through the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) 31 , and of depressive symptoms, measured with the Hamilton Depression Rating Scale (HAM-D) 32 . Quality of life Quality of life was assessed through the Italian validation of the 36-Item Short Form Survey (SF-36) 33 . The arithmetic mean of the scores obtained in the nine scales of SF-36 was used as a global measure to compare the quality of life between non-frail and frail groups. We used the General Health scale score of SF-36 as a single frailty domain to be correlated with serum amino acids levels, since it is the SF-36 scale semantically closer to the General Health Status item of the EFS 9 . Sarcopenia and visceral adiposity Body composition (fat mass (FM) and fat-free mass (FFM)) was evaluated using fan-beam dual‐energy X‐ray absorptiometry (DXA) (Lunar Prodigy DXA, GE Medical Systems). The in vivo coefficients of variation were 0.89% and 0.48% for FM and FFM, respectively. Skeletal Muscle Index (SMI) was calculated as the sum of fat-free soft tissue mass of arms and legs divided for height squared 34 . Visceral adipose tissue (VAT) volume was estimated using a constant correction factor (0.94 g/cm 3 ). The software automatically placed a quadrilateral box, representing the android region, outlined by the iliac crest and with a superior height equivalent to 20% of the distance from the top of the iliac crest to the base of the skull 35 . Functional performance and independence Handgrip strength test was performed using a Jamar dynamometer adhering to the standardized protocol recommended by the American Society of Hand Therapists 36 . Handgrip measurement was assessed on the dominant hand and was considered “strong” or “weak” based on sex and body mass index (BMI)-adjusted cut-off scores, as previously described 8 . Basic Activities of Daily Living (BADL) and Independent Activities of Daily Living (IADL) were measured by interviewing the patients and caregivers 37 . Nutritional status Nutritional status was evaluated with Mini Nutritional Assessment (MNA), which is composed of 18 items divided in four categories: anthropometric assessment, general state, dietary assessment and self-assessment. A score ≥ 24 points indicates a good nutritional status; a score between 17 and 23.5 points indicates risk of malnutrition, while a score ≤ 17 points indicates malnutrition 38 . Frailty Frailty was separately evaluated with the EFS and the frailty phenotype. The EFS assesses nine frailty domains frailty (cognition, general health, functional independence, social support, medication usage, nutrition, mood, continence, functional performance) 9 . EFS score ranges from 0 to 17. Participants were classified as “non-frail” (EFS ≤ 5) or “frail” (EFS > 5) according to previously proposed cut-off 10 . Since only three subjects had an EFS score > 11 (used to define the “severe frail” category 10 ), we considered all the subjects with an EFS score > 5 as a single “frail” group. The physical frailty phenotype contains 5 criteria, including weight loss, exhaustion, low physical activity, slow walking speed and low grip strength 8 . Participants who met 3 or more criteria were defined “frail”, those who met 1 or 2 criteria were classified as “pre-frail” and those who met no criteria were defined “non-frail”. Collection and storage of serum samples Blood sampling was performed after a 12-hour fasting. Whole blood was collected by peripheral venipuncture into clot activator tubes and gently mixed. Sample was stored upright for 30 min at room temperature to allow blood to clot, and centrifuged at 2000 × g for 10 min at room temperature. Serum was aliquoted (0.5 ml) in polypropylene cryotubes and stored at − 80°C before usage. HPLC analysis of amino acids content Serum samples (100 µl) were mixed in a 1:10 dilution with HPLC-grade methanol (900 µl) and centrifuged at 13,000xg for 10 min; supernatants were dried and then suspended in 0.2 M trichloroacetic acid (TCA). TCA supernatants were then neutralized with 0.2 M NaOH and subjected to precolumn derivatization with o-phthaldialdehyde /N-acetyl-L-cysteine in 50% methanol. Amino acids derivatives were resolved on a UHPLC Nexera X3 system (Shimadzu) by using a Shim-pack GIST C18 3-µm reversed-phase column (Shimadzu, 4.0x150 mm) under isocratic conditions (0.1 M sodium acetate buffer, pH 6.2, 1% tetrahydrofuran, and 1 ml/min flow rate). A washing step in 0.1 M sodium acetate buffer, 3% tetrahydrofuran and 47% acetonitrile, was performed after every run. Identification and quantification of amino acids were based on retention times and peak areas, compared with those associated with external standards. The detected amino acids concentration was expressed as µM. Statistical analyses Clinical and demographic characteristics were described using, as summary statistics, median and the interquartile range (IQR) or absolute and relative frequencies. The comparison of clinical-demographic features between non-frail and frail groups were performed with Mann-Whitney U test (for binary EFS-based stratification) or Kruskal-Wallis test (for the three frailty-phenotype categories) for continuous variables and Chi-square test for categorical variables. The normality of data distribution was checked with the Kolmogorov–Smirnov test. Due to non-normal distribution, the serum amino acid levels were log 10 -transformed and then compared between frail and non-frail groups using a four-way ANCOVA model with “frailty status”, “sex”, “type 2 diabetes” and “smoking” as factors and “age” and “BMI” as covariates. Levene’s test was used to check the equality of variances between groups. The correlation of serum amino acid concentration with age was evaluated with Spearman’s correlation test. Partial correlation analyses adjusted for the effect of age and sex were adopted to test the correlation between serum amino acid levels and EFS score and the other clinical variables. To assess the ability of serum amino acids levels to predict EFS score, we used multiple linear regression models including age, sex, the clinical predictors of EFS score 10 and the single amino acid concentrations as predictors and EFS score as dependent variable. To evaluate the ability of serum amino acids to predict the physical frailty phenotype 8 we adopted multinomial logistic regression models using age, sex and the single amino acid concentration as predictors and frailty category as dependent variable. For linear regression analyses, we verified that the residuals were normally distributed, there was no heteroscedasticity and no multicollinearity between the variables (variance inflation factor < 5). The latter was also evaluated in the logistic regression analyses. Significance was set at p < 0.05 for all analyses. Data were analysed by using SPSS 26.0 software (IBM, Armonk, NY, USA). Results Participants One-hundred and twenty-five consecutive elderly subjects were enrolled in the study. The participants were stratified in non-frail (n = 74) and frail (n = 51) groups accordingly to EFS score 10 . Demographic and clinical features of study participants are reported in Table 1 A. Frail subjects were older and showed higher females prevalence than non-frail participants. As expected, frail group showed worse performance in physical, sarcopenia, cognitive, nutritional, functional independence, and quality of life domains. Total medication count was higher in frail compared to non-frail group. The proportions of patients with type 2 diabetes mellitus and of current/former/never smokers was similar between non-frail and frail subjects. MMSE and MoCA scores did not correlate with age in either non-frail (r = 0.214, p = 0.075 and r = -0.013, p = 0.918, respectively) or frail group (r = − 0.180, p = 0.220 and r = -0.236, p = 0.106, respectively), indicating that the difference in MoCA score between non-frail and frail groups was not attributable to the older age of frail subjects. Serum levels of D-serine and D-/Total serine ratio correlate with EFS score We first investigated whether the serum levels of amino acids were different between frail and non-frail groups adjusting for the effect of the potential confounders. ANCOVA showed no between-group differences in D-serine, L-serine or any of the other amino acids level (Table 1 B). To further address this issue, we measured the partial correlation between the quantitative EFS score and the serum concentrations of amino acids, adjusting for age and sex. We found a significant mild positive correlation of EFS with serum D-serine (r = 0.197, p = 0.032) and D-/Total serine ratio (r = 0.213, p = 0.020), but not the other amino acids (Fig. 1 and Suppl. Table 1). Correlation of Serum levels of D-serine and D-/Total serine ratio with demographic and clinical features We also investigated whether diabetes, obesity (BMI, VAT), sarcopenia (SMI) and cigarette smoking affected the serum concentration of amino acids. Diabetic subjects showed higher levels of L-asparagine, L-serine, L-glutamate, L-glutamine/L-glutamate ratio than non-diabetic participants (Suppl. Table 2). After adjustment for age and sex, L-glutamate and L-Glutamine/L-Glutamate correlated with (i) BMI and VAT in both non-frail and frail participants; (ii) SMI only in the non-frail group (Suppl. Table 3). Current and former smokers had reduced L-glutamine/L-glutamate ratio compared to never smokers (Suppl. Table 4). Serum D-serine correlated with age in the frail (r = 0.299, p = 0.033) but not in non-frail group, while D-/Total serine ratio correlated with age both in non-frail (r = 0.278, p = 0.017) and frail subjects (r = 0.415, p = 0.002) (Fig. 2 and Suppl. Table 5). Serum levels of D-serine and D-/Total serine ratio are independent predictors of frailty Furthermore, to assess whether serum levels of D-serine and D-/Total serine ratio are independently associated with frailty, we performed multiple linear regression models using the quantitative EFS score as dependent variable and the known clinical predictors of EFS 10 , added to the individual amino acids concentrations, as predictors. Interestingly, increased levels of D-serine and D-/Total serine ratio, but not the other amino acids, resulted to be independent predictors of EFS score, along with older age and worse nutritional status, handgrip, global cognition and higher number of drugs (Table 3 and Suppl. Tables B-H). These findings highlight that an abnormally greater serum D-/Total serine ratio, used as an index of D-serine metabolism 39 , along with blood D-serine concentrations, may represent a novel systemic biochemical signature of frailty in elderly people. Table 1 (A) Clinical and demographic features of elderly cohort considered as a whole and after stratification by frailty status according to EFS. (B) Serum amino acid levels in elderly cohort considered as a whole and after stratification by frailty status. Data are shown as median (IQR) or absolute frequency (%) for continuous and categorical variables, respectively. The total number of non-frail (NF) and frail (FR) subjects for which data were available is reported in the second column. A) Frail vs non-frail: clinical-demographic features N Total Non-frail Frail p Age, years 74 NF, 51 FR 74.0 (69.5–81.0) 72.0 (68.0–75.0) 81.0 (75.0–85.0) < 0.001 a Female sex, n (%) 74 NF, 51 FR 95 (76.0) 51 (68.9) 44 (86.3) 0.026 b SPPB total score 74 NF, 51 FR 8.0 (5.0–10.0) 9.0 (8.0–10.0) 4.0 (3.0–7.0) < 0.001 a Handgrip (kg) 74 NF, 51 FR 20.0 (16.0–26.0) 24.0 (20.0-32-0) 16.0 (12.0–20.0) < 0.001 a SMI (kg/m 2 ) 74 NF, 51 FR 7.6 (7.1–8.6) 8.1 (7.1–8.9) 7.5 (6.9–8.1) 0.012 a MMSE 70 NF, 48 FR 27.1 (26.0-27.7) 27.2 (26.2–27.7) 27.1 (25.7–27.7) 0.374 a MoCA 70 NF, 48 FR 24.1 (21.5–26.1) 25.3 (23.4–26.7) 21.4 (19.7–25.1) < 0.001 a MNA 74 NF, 50 FR 23.8 (20.6–25.5) 25.0 (23.5–26.0) 20.5 (18.5–23.1) < 0.001 a BADL 72 NF, 46 FR 6.0 (6.0–6.0) 6.0 (6.0–6.0) 6.0 (5.0–6.0) 0.001 a IADL 72 NF, 46 FR 8.0 (6.0–8.0) 8.0 (8.0–8.0) 6.0 (4.0–8.0) < 0.001 a HAM-D 72 NF, 46 FR 5.0 (2.0–10.0) 5.0 (2.0-9.8) 4.0 (2.0–12.0) 0.797 a SF-36 (mean score) 71 NF, 46 FR 66.8 (52.7–78.3) 73.7 (57.4–81.6) 61.9 (38.1–67.4) < 0.001 a Number of drugs 72 NF, 49 FR 4.0 (2.5-8.0) 3.0 (2.0–5.0) 7.0 (5.0-11.5) < 0.001 a Type 2 diabetes, n (%) 74 NF, 51 FR 21 (16.8) 10 (13.5) 11 (21.6) 0.236 b BMI (kg/m 2 ) 74 NF, 51 FR 27.7 (24.2–32.5) 27.9 (24.2–31.7) 27.6 (23.7–33.3) 0.752 a VAT (g) 73 NF, 51 FR 1035 (548–1557) 1049 (530–1652) 960 (555–1502) 0.463 a Current smokers, n (%) 74 NF, 51 FR 15 (12.0) 9 (12.2) 6 (11.8) 0.152 b Former smokers, n (%) 74 NF, 51 FR 22 (17.6) 9 (12.2) 13 (25.5) Never smokers, n (%) 74 NF, 51 FR 88 (70.4) 56 (75.7) 32 (62.7) EFS 74 NF, 51 FR 4.0 (2.0–7.0) 2.0 (1.0–4.0) 8.0 (6.0–9.0) < 0.001 a B) Frail vs non-frail: serum amino acid levels N Total (n = 125) Non-frail (n = 74) Frail (n = 51) p c L-aspartate (µM) 74 NF, 51 FR 4.0 (3.0-5.6) 3.9 (3.0-5.5) 4.4 (3.1–6.5) 0.409 L-asparagine (µM) 74 NF, 51 FR 24.1 (19.8–34.3) 24.8 (20.8–28.0) 22.6 (19.2–28.8) 0.676 Glycine (µM) 74 NF, 51 FR 208.9 (174.0-288.2) 201.0 (161.8-268.3) 222.3 (185.6-400.4) 0.223 D-serine (µM) 74 NF, 51 FR 1.9 (1.6–2.3) 1.8 (1.5–2.1) 2.1 (1.7–2.6) 0.167 L-serine (µM) 74 NF, 51 FR 72.5 (60.0-88.9) 75.8 (62.3–89.5) 71.1 (54.5–85.1) 0.963 D-/Total serine (%) 74 NF, 51 FR 2.5 (2.0-3.2) 2.3 (1.9–2.8) 2.8 (2.2-4.0) 0.181 L-glutamate (µM) 74 NF, 51 FR 26.7 (19.1–34.3) 25.3 (18.8–33.7) 28.3 (19.2–34.6) 0.299 L-glutamine (µM) 74 NF, 51 FR 323.0 (280.5-370.3) 325.3 (282.0-365.7) 316.5 (276.3-381.5) 0.456 L-glutamine/L-glutamate 74 NF, 51 FR 12.1 (9.8–16.7) 12.6 (10.1–16.6) 11.2 (9.8–17.4) 0.578 a Mann-Whitney U test b Chi Square test c Four-way ANCOVA with frailty status, sex, diabetes and smoking as factors, age and BMI as covariates. The analysis was conducted on log-transformed amino acid concentrations to normalize the data distribution. Log-transformed values are reported as Suppl. Table A in Zenodo repository DOI: 10.5281/zenodo.10669703 ). Table 2 Multiple linear regression models for EFS prediction, including clinical variables and serum D-serine (model 1) or D-/Total serine ratio (model 2) as predictors. Complete clinical data were available for n = 110 subjects. Model 1: D-Serine and clinical features as predictors of EFS β SE Std β p Constant 3.087 3.833 0.422 Age (years) 0.115 0.036 0.255 0.002 Male sex 0.214 0.649 0.029 0.742 MNA -0.159 0.068 -0.167 0.022 Handgrip (kg) -0.087 0.034 -0.260 0.011 BADL 0.095 0.269 0.031 0.723 IADL -0.170 0.168 -0.101 0.313 HAM-D 0.021 0.030 0.042 0.477 MoCA -0.144 0.063 -0.151 0.025 Number of drugs 0.170 0.056 0.215 0.003 D-serine (µM) 0.704 0.321 0.136 0.031 Model 2: D-/Total serine and clinical features as predictors of EFS β SE Std β p Constant 4.635 3.822 0.228 Age (years) .102 .038 .226 0.008 Male sex .201 .645 .027 0.756 MNA − .198 .069 − .208 0.005 Handgrip (kg) − .083 .034 − .248 0.015 BADL .065 .268 .021 0.809 IADL − .125 .168 − .075 0.458 HAM-D .017 .030 .033 0.574 MoCA − .140 .063 − .147 0.029 Number of drugs .167 .056 .212 0.003 D-/Total serine (%) .553 .228 .162 0.017 Table 3 Correlations between the serum levels of amino acids and HAM-D score in the elderly cohort stratified in non-frail and frail groups according to EFS. HAM-D score was available for 72 non-frail and 46 frail participants. Correlation coefficients and p-values refer to age and sex-adjusted partial correlations. L-aspartate L-asparagine Glycine D-serine L-serine D-/Total serine L-glutamate L-glutamine L-glutamine/L-glutamate r p r p r p r p r p r p r p r p r p Non-frail -0.073 0.549 -0.019 0.876 -0.004 0.976 -0.026 0.833 -0.030 0.804 0.013 0.918 0.057 0.638 0.014 0.906 -0.063 0.603 Frail 0.012 0.938 -0.403 0.007 0.389 0.009 -0.052 0.736 -0.241 0.116 0.131 0.395 0.067 0.667 -0.347 0.021 -0.255 0.094 Increased serum glycine and D-/Total serine ratio correlate with worse global cognition in frail elderly subjects Next, we investigated whether serum D-serine, D-/Total serine ratio and the other amino acids were associated with one or more of the frailty domains which concur to determine the EFS score. Notably, we found negative partial correlations between (i) glycine, D-/Total serine ratio and MMSE; (ii) glycine and MoCA score in the frail but not in the non-frail subjects (Fig. 3 ). The other amino acids did not correlate with cognitive measures (Suppl. Figure 1 and Suppl. Table I). Moreover, L-asparagine and L-glutamine correlated negatively with HAM-D score, while glycine levels increased with worse depressive symptoms in frail but not in non-frail subjects (Table 4). There were no significant correlations between the serum amino acids and the other frailty domains (Suppl. Tables J-K). Overall, these findings indicate that dysregulated blood glycine amount and D-/Total serine ratio may represent a metabolic signature of cognitive impairment and depressive symptoms in frail older subjects. Serum D-serine and D/Total serine do not correlate with physical frailty phenotype To further evaluate the relationship between serum amino acids and frailty, we stratified the elderly cohort according to Fried’s frailty phenotype 8 . Based on these criteria, 22 subjects were classified as non-frail, 51 as pre-frail and 52 as frail. After adjusting for the effect of potential confounders, there were no significant differences in the serum concentrations of the tested amino acids between the 3 groups (Suppl. Table 6). To better assess whether the serum levels of these metabolites may associate with physical frailty phenotype, we performed multinomial logistic regression models with Fried phenotype as dependent variable and age, sex and the individual amino acids concentrations as predictors. Remarkably, we found that neither the levels of D-serine, nor those of the other amino acids, were associated with physical frailty or pre-frailty status (Suppl. Table 7 and Suppl. Table L). Taken together, these findings suggest that the blood levels of D-serine and D-/Total serine ratio are not associated with the physical domain of frailty in elderly individuals. The correlations between serum D-serine, D-/Total serine ratio and EFS are driven by female sex The demographic and clinical features of the elderly cohort stratified by sex are reported in Suppl. Table 8A. Females showed a worse impairment in physical and quality of life domains compared to males. Although the difference was not statistically significant, females also had a higher EFS score than males. The serum concentrations of amino acids were similar between females and males (Suppl. Table 8B). D-serine and D/Total serine ratio positively correlated with age in both sexes, while L-serine selectively decreased with older age only in males (Suppl. Table M). Consistent with these HPLC data, we found that the positive correlation between D-serine, D-/Total serine and EFS score observed in the whole cohort (Fig. 1 ) was mainly driven by female sex (Suppl. Table N). The multiple linear regression models adjusted for the clinical predictors of EFS showed that the levels of D-serine and D-/Total serine, but not the other amino acids, were independent predictors of EFS score in females (β = 0.989, p = 0.008 and β = 0.748, p = 0.007, respectively) but not in males (Suppl. Table O). Increased serum glycine levels and D-/Total serine ratio correlate with worse global cognition and quality of life in a sex-dependent manner We found a negative correlation between D-serine (r = -0.222, p = 0.035) and D-/Total serine (r = -0.289, p = 0.006) and MMSE score in females but not in males. Moreover, D-/Total serine negatively correlated with MoCA (r = -0.235, p = 0.025), SF-36 General Health (r = -0244, p = 0.021) and SPPB total score (r = -0.209, p = 0.043) in females but not in males. Finally, L-asparagine and L-glutamine correlated negatively with HAM-D score, while glycine increased with worse depressive symptoms in females but not in males (Suppl. Tables P-W). Discussion Compelling studies have shown that changes in the cerebrospinal fluid (CSF) and blood levels of amino acids acting on the glutamatergic N-methyl-D-aspartate receptor (NMDAR) represent a neurochemical signature in various neuropathologies. These include psychiatric conditions such as schizophrenia 39,40 and major depression 41 , and a wide spectrum of neurological diseases, including Alzheimer’s disease (AD) 42–44 , frontotemporal dementia 45 , Parkinson’s disease (PD) 46–49 , amyotrophic lateral sclerosis 50,51 , mild cognitive impairment 52,53 , multiple sclerosis 54,55 and traumatic brain injury 56 . Surprisingly, no investigation so far specifically addressed the relationship between these neuroactive molecules and frailty phenotypes, including those related to cognitive decline and depression. Here, we sought to fill this gap by investigating the endogenous levels of D-serine, glycine and the other amino acids acting on glutamatergic neurotransmission in a well-characterized cohort of older subjects encompassing the entire continuum existing between fit and frail aging. Overall, our biochemical determinations suggest that disrupted systemic D-serine homeostasis may represent a potential predictive biomarker of frailty, while increased serum glycine and D-/Total serine ratio could be specifically associated with cognitive decline and depression in frail elderly individuals. Previous blood metabolomics studies identified several metabolites associated with frailty, belonging to redox homeostasis, inflammation, amino acids, purine metabolism, urea and tricarboxylic acid cycles and sugar metabolism pathways 14 . Among the amino acids identified as dysregulated, glutamate metabolism was found to be affected in frail compared to non-frail subjects 16–18,21,25 . In light of this finding, and given the close relationship linking frailty with cognitive decline 12,27,57 , we investigated whether the serum levels of amino acids acting on glutamatergic NMDAR and their precursors could predict frailty status, and specifically its cognitive domain, in elderly adults. Interestingly, we found that serum D-serine is an independent predictor of the EFS score. D-serine is synthetized by serine racemase (SR) 58 starting from its L-enantiomer and then degraded through D-amino acid oxidase (DAO) activity 59,60 . Once released in the forebrain, D-serine act as an obligatory co-agonist at the glycine modulatory site on GluN1 subunit of NMDAR, a ionotropic glutamatergic receptor playing a key role in sensorimotor gating, synaptic plasticity and cognitive functions 61 . Despite a few reports suggested that circulating blood D-serine concentration decrease 61 or remains unchanged 49,62,63 during healthy aging, recent studies found a positive correlation between serum D-serine and age in patients affected by AD and PD 49,63 . Our observations showing that D-serine and D-/Total serine ratio significantly increase with ageing in frail but not in non-frail controls suggests that a dysregulation of blood D-serine homeostasis may represent a common ageing-related metabolic variation across different neuropathologies. While EFS was conceived to evaluate frailty through a multidimensional approach, the Fried’s frailty phenotype is a widely used tool able to capture the physical domain of frailty 8 . Notably, we failed to find any association between D-serine or the other amino acids levels and frailty phenotype. Therefore, we argue that D-serine may not mirror all the components of frailty syndrome, but could instead represent a specific biochemical fingerprint of its cognitive domain. Consistent with this view, D-serine levels have recently been proposed as an early gender-related biomarker of AD since their serum concentrations correlated with cognitive deterioration in female patients 44,63 . However, other Authors failed to confirm significant changes of CSF and blood D-Ser levels in the whole AD clinical spectrum 42,64 . Interestingly, a recent clinical-pathological study showed that Aβ and tau brain deposition and frailty have a synergistic impact in determining the onset of dementia 57 . This finding, considered together with (i) the previous studies linking increased D-serine with AD-related pathology and cognitive decline 43,65,66 and (ii) the ability of D-serine to diffuse across the blood-brain barrier 67 , suggest that blood levels of this D-amino acid could be adopted as a metabolic signature to identify older adults at higher risk of conversion to dementia. Notably, the stratification of our elderly cohort by sex disclosed that the correlation between serum D-serine, EFS score and global cognition was mainly driven by females. In agreement with this view, recent investigations showed increased D-/Total Ser ratio in the human post-mortem hippocampus and serum of AD female patients compared to healthy females 66,68 . Similarly, we recently found a significant increase of serum D-serine in PD female, but not in male patients, compared to healthy controls 49 . These findings suggest that a dysregulation of blood D-serine may reflect the occurrence of different neuropathologies in a sex-dependent manner. Considering the neuroprotective roles played by estrogens and the compelling evidence that estrogens loss after menopause can accelerate the effect of aging on cognitive functions 69 , we speculate that the link between increased systemic D-serine levels and cognitive decline may be mediated, at least in part, by the reduced estrogens levels which characterize females aging. However, further studies on larger elderly cohorts are needed to address this outstanding issue. We also found that higher serum glycine concentrations correlated with worse cognitive function and depressive symptoms in the frail but not in the non-frail group. Similarly to D-serine, glycine binds the GluN1 subunit of NMDAR and acts as a major obligatory co-agonist 70 . However, in other central nervous system (CNS) regions, glycine also regulates inhibitory neurotransmission via glycine receptors (GlyR) 71 . Considering that glycinergic transmission has been implicated in the physiopathology of cognitive decline and depression 70,71 , the correlation between blood glycine levels, cognitive performance and depressive symptoms may mirror a dysregulation of this amino acid in the CNS of frail subjects. Despite our biochemical data are highly intriguing, the findings of the present study should be interpreted cautiously, bearing in mind that (i) the assessment of AD and other dementia-related biomarkers was not included in this study, thus preventing any inference linking the serum amino acid levels and the presence of concomitant neurodegenerative diseases; (ii) frailty is characterized by a decline in the function of multiple organ systems, which may directly influence the serum concentration of D-serine, glycine and the other amino acids. Indeed, recent studies showed that blood D-serine levels correlate positively with biochemical renal parameters 62,72–74 , while various L-amino acids correlated with metabolic parameters such as liver enzymes, lipids and blood glucose 62 . In the kidney and liver, glycine is rapidly interconverted with L-serine, which may then enter the glycolytic or gluconeogenic pathways through its conversion in pyruvate, or be employed in phospholipids biogenesis or mitochondrial metabolism 75 . Dietary intake and D-amino acids produced by the gut microbiota may also affect serine enantiomers metabolism 76,77 . Interestingly, studies in animal models showed that D-serine is detectable in multiple organs, including heart, pancreas, spleen, liver, kidney, lung and muscles 78,79 , and glutamatergic receptors play relevant functions in the modulation of physiological processes in several peripheral tissues 80 . Of note, recent studies showed that serine racemase and NMDAR are highly expressed in human pancreatic islet β cells 81 , and systemic D-serine administration modulates insulin secretion in a dose-dependent manner 82,83 . Despite we found similar serum D-serine levels between diabetic and non-diabetic subjects, L-serine and L-glutamate were increased in diabetic compared to non-diabetic group, consistently with previous blood metabolomics evidence 17,25 . In line with other studies 84 , we also observed a positive correlation between serum L-glutamate concentration, BMI and visceral adiposity in both non-frail and frail participants. Although the biological mechanisms responsible for this association are still unclear, considering that glutamate signalling modulates the immune system 85 and that increased VAT promotes systemic inflammation 86 , elevated blood L-glutamate levels could represent a metabolic signature underpinning the abnormal increase in oxidative stress and inflammation associated to obesity. Concurrently, our data suggest a correlation between serum L-glutamate concentration and SMI. This is in line with previous investigations showing that glutamate is crucial in maintaining the homeostasis of energy metabolism in skeletal muscle 87 . Surprisingly, this relationship was observed in the non-frail but not in frail group, suggesting that different biological pathways may modulate the maintenance of skeletal muscle mass across healthy and frail aging. However, our results may be affected by the very low prevalence of subjects with SMI scores below the proposed cut-off to define sarcopenia 34 and therefore require validation in larger cohorts. Besides its neuroactive role, glycine primarily influences anti-oxidative reactions and immune system 75 . In agreement with this knowledge, glycine has been used to prevent tissue injury, enhance anti-oxidative capacity, improve immunity, and treat metabolic disorders in obesity, diabetes and various inflammatory diseases 88 . Thus, consistent with the multiple beneficial effects of glycine, we cannot rule out that the negative correlation between this amino acid and cognitive functions in frail older individuals might represent an epiphenomenon triggered by inflammation and metabolic dysfunctions, rather than being causally linked to memory impairments. In the same way, we cannot rule out that systemic D-serine metabolism variation in frailty may represent a biochemical adaptation to CNS and multi-system deteriorations. In line with this hypothesis, D-serine supplementation or treatment with DAO inhibitors significantly improved cognitive functions in healthy subjects, PD and schizophrenia patients 89–92 . Future investigations are warranted to clarify these important issues. The strengths of our work include (i) the novelty of investigating NMDAR-related amino acids and their precursors in the serum of a well-characterized elderly cohort, including the entire clinical spectrum existing from fit to frail condition; (ii) the correlation of serum amino acids with multiple potential confounding factors, such as diabetes, body composition and cigarette smoking 93 ; (iii) the assessment of frailty with two different but complementary tools 8,9 . However, we also acknowledge some limitations. First, the cross-sectional design and the clinical-biochemical correlations observed in the present study did not allow to draw any causal relationship between the serum changes in amino acids levels and the clinical phenotypes. Future longitudinal studies on larger elderly cohorts adopting a multidimensional approach, including the measurement of blood biomarkers mirroring brain (e.g. neurofilament light chain, Aβ42/Aβ40 ratio, phosphorylated tau 94 ) and peripheral organs damage, as well as inflammation, are warranted to elucidate this issue. Second, the sex ratio was unbalanced with an higher prevalence of females, potentially biasing the analyses conducted after stratifying the cohort by sex. Third, the assessment of biochemical parameters of kidney and liver function was not included in the study protocol, thus preventing the adjustment of the analyses for the serum levels of creatinine, aspartate transaminase and alanine transaminase, which correlate with the blood levels of D-Ser and several L-amino acids, respectively 62 . However, the history of any kidney or liver disease or altered parameters of renal and hepatic function was strictly considered as an exclusion criteria at the time of participants enrolment. In conclusion, this study highlights serum D-/serine and glycine as putative biochemical signatures of cognitive decline and depression in frail older subjects. The observation that D-serine correlates with frailty scores and global cognition in females but not in males suggest that this effect may also be modulated by sex-related biological factors. Declarations Acknowledgments The authors are grateful to all the patients, their caregivers and the research subjects involved in this project. Conflict of interest The Authors declare no competing financial interest related to this manuscript. Other disclosures are reported below. Alberto Imarisio reports no disclosures. Isar Yahyavi reports no disclosures. Clara Gasparri reports no disclosures. Amber Hassan reports no disclosures. Micol Avenali is member of the Monogenic Network of ASAP GP2 (Global Parkinson genetic Program); received speaker's honoraria from Bial Italia and Zambon. She received research funding for research projects from the Italian Ministry of Health. Anna Di Maio reports no disclosures Gabriele Buongarzone reports no disclosures. Caterina Galandra reports no disclosures. Marta Picascia reports no disclosures. Asia Filosa reports no disclosures. Maria Cristina Monti reports no disclosures. Claudio Pacchetti reports no disclosures. Francesco Errico reports no disclosures. Mariangela Rondanelli reports no disclosures. Alessandro Usiello reports no disclosures. Enza Maria Valente is Associate Editor of Journal of Medical Genetics; is Genetics Section Editor of Pediatric Research, of The Cerebellum, and of Neurological Sciences; is member of the Editorial Board of Movement Disorders Clinical Practice; is member of the Steering Committee of ASAP GP2 (Global Parkinson genetic Program). E.M.V. received research support from the Italian Ministry of Health, CARIPLO Foundation, Telethon Foundation Italy, Pierfranco and Luisa Mariani Foundation, and European Commission (Eranet Neuron). Funding This work was partially supported by grants from CARIPLO Foundation (grant nr. 2017-0575 to EMV and AU), Italian Ministry of Health (Ricerca Corrente 2023 to IRCCS Mondino Foundation) and Italian Ministry of University and Research (PRIN 2022 - COD. 2022XF7YYL_02 to AU). The work of E.M.V. and A.U. is supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). Author contributions: Alberto Imarisio: Data curation; Formal analysis; Investigation; Writing - original draft; Writing - review & editing. Isar Yahyavi: Data curation; Investigation; Methodology; Writing - review & editing. Clara Gasparri: Data curation; Investigation; Methodology; Writing - review & editing. Amber Hassan: Data curation; Investigation; Methodology; Writing - review & editing. Micol Avenali: Data curation; Writing - review & editing Anna Di Maio: Data curation; Investigation; Writing - review & editing. Gabriele Buongarzone: Data curation; Formal analysis; Writing - review & editing Caterina Galandra: Data curation; Writing - review & editing Marta Picascia: Data curation; Writing - review & editing Asia Filosa: Formal analysis; Writing - review & editing Maria Cristina Monti: Formal analysis; Writing - review & editing Claudio Pacchetti: Data curation; Writing - review & editing Francesco Errico: Data curation; Investigation; Writing - review & editing Mariangela Rondanelli: Data curation; Investigation; Methodology; Writing - review & editing Alessandro Usiello: Conceptualization; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing - review & editing Enza Maria Valente: Conceptualization; Funding acquisition; Investigation; Project administration; Resources; Supervision; Validation; and Writing - review & editing. All authors approved the final version of the manuscript. 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Lockridge A, Gustafson E, Wong A, Miller RF, Alejandro EU. Acute D-Serine Co-Agonism of β-Cell NMDA Receptors Potentiates Glucose-Stimulated Insulin Secretion and Excitatory β-Cell Membrane Activity. Cells 2021; 10: 93. Suwandhi L, Hausmann S, Braun A, Gruber T, Heinzmann SS, Gálvez EJC et al. Chronic D-serine supplementation impairs insulin secretion. Mol Metab 2018; 16: 191–202. Chaouche L, Marcotte F, Maltais-Payette I, Tchernof A. Glutamate and obesity - what is the link? Curr Opin Clin Nutr Metab Care 2024; 27: 70–76. Levite M. Glutamate, T cells and multiple sclerosis. J Neural Transm 2017; 124: 775–798. Alexopoulos N, Katritsis D, Raggi P. Visceral adipose tissue as a source of inflammation and promoter of atherosclerosis. Atherosclerosis 2014; 233: 104–112. Nakajima H, Okada H, Kobayashi A, Takahashi F, Okamura T, Hashimoto Y et al. Leucine and Glutamic Acid as a Biomarker of Sarcopenic Risk in Japanese People with Type 2 Diabetes. Nutrients 2023; 15: 1–11. Soh J, Raventhiran S, Lee JH, Lim ZX, Goh J, Kennedy BK et al. The effect of glycine administration on the characteristics of physiological systems in human adults: A systematic review. GeroScience 2023; 46: 219–239. Lin C-H, Lin C-H, Chang Y-C, Huang Y-J, Chen P-W, Yang H-T et al. Sodium Benzoate, a D-Amino Acid Oxidase Inhibitor, Added to Clozapine for the Treatment of Schizophrenia: A Randomized, Double-Blind, Placebo-Controlled Trial. Biol Psychiatry 2018; 84: 422–432. Levin R, Dor-Abarbanel AE, Edelman S, Durrant AR, Hashimoto K, Javitt DC et al. Behavioral and cognitive effects of the N-methyl-d-aspartate receptor co-agonist d-serine in healthy humans: Initial findings. J Psychiatr Res 2015; 61: 188–195. Heresco-Levy U. N-Methyl-D-aspartate (NMDA) receptor-based treatment approaches in schizophrenia: the first decade. Int J Neuropsychopharmacol 2000; 3: S1461145700001978. Gelfin E, Kaufman Y, Korn-Lubetzki I, Bloch B, Kremer I, Javitt DC et al. D-serine adjuvant treatment alleviates behavioural and motor symptoms in Parkinson’s disease. Int J Neuropsychopharmacol 2012; 15: 543–549. Xu T, Holzapfel C, Dong X, Bader E, Yu Z, Prehn C et al. Effects of smoking and smoking cessation on human serum metabolite profile: Results from the KORA cohort study. BMC Med 2013; 11: 1–14. Arslan B, Zetterberg H, Ashton NJ. Blood-based biomarkers in Alzheimer’s disease – moving towards a new era of diagnostics. Clin Chem Lab Med 2024. doi: 10.1515/cclm-2023-1434 . Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files Suppl.Fig.1.tif suppltables.docx suppltableszenodo.docx Cite Share Download PDF Status: Published Journal Publication published 09 Jul, 2024 Read the published version in Translational Psychiatry → Version 1 posted Editorial decision: revise 14 May, 2024 Review # 3 received at journal 01 Apr, 2024 Review # 1 received at journal 25 Mar, 2024 Reviewer # 3 agreed at journal 18 Mar, 2024 Review # 2 received at journal 16 Mar, 2024 Reviewer # 2 agreed at journal 14 Mar, 2024 Reviewer # 1 agreed at journal 14 Mar, 2024 Reviewers invited by journal 14 Mar, 2024 Submission checks completed at journal 29 Feb, 2024 First submitted to journal 28 Feb, 2024 Unknown event 28 Feb, 2024 Editor assigned by journal 27 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":104254,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between the serum levels of amino acids and Edmonton Frailty Scale (EFS) total score in the whole elderly cohort. Blue lines and grey shadows represent the best fit line and its 95% CI, respectively. *p \u0026lt; 0.05, age- and sex-adjusted partial correlation.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3994211/v1/0caf0cb1ed22f36bd512e2ee.png"},{"id":52891551,"identity":"6d8bc953-9ff3-46d4-bc08-61797706fcd1","added_by":"auto","created_at":"2024-03-18 11:41:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141332,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between the serum amino acids concentrations and age in elderly cohort stratified in frail and non-frail groups according to EFS. Blue lines and grey shadows represent the best fit line and its 95% CI, respectively. * p \u0026lt; 0.05; **p \u0026lt; 0.01, Spearman’s correlation test.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3994211/v1/2c5f8c22d8fd85dbe6e18d45.png"},{"id":52891552,"identity":"5948bd01-769b-4b6a-b7b8-19d87dc8aa4a","added_by":"auto","created_at":"2024-03-18 11:41:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132971,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between the serum amino acids concentrations and measures of global cognition in elderly cohort stratified in frail and non-frail groups according to EFS. Blue lines and grey shadows represent the best fit line and its 95% CI, respectively. * p \u0026lt; 0.05; **p \u0026lt; 0.01, age and sex-adjusted partial correlations. Abbreviations: MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3994211/v1/3af99549b4f8ad9dd7fa92e8.png"},{"id":59985073,"identity":"cc8946b6-0bc0-414c-b6d6-727d6d65295b","added_by":"auto","created_at":"2024-07-10 07:06:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1681934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3994211/v1/a929f3d5-7c9a-4070-b4b6-27b5262cf3e8.pdf"},{"id":52891553,"identity":"62afdd7a-f01e-4058-a79b-195f680dec8b","added_by":"auto","created_at":"2024-03-18 11:41:56","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1171020,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.Fig.1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3994211/v1/e3d1a61d0c0c52d5b9a8e1e5.tif"},{"id":52891550,"identity":"a8a8e3de-b2d8-4e28-9815-1953fe09b667","added_by":"auto","created_at":"2024-03-18 11:41:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":42990,"visible":true,"origin":"","legend":"","description":"","filename":"suppltables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3994211/v1/a924023e15ebbd0e1247afcd.docx"},{"id":52891855,"identity":"4c5bfcf0-23af-47e8-b6e3-d0de9dc2918d","added_by":"auto","created_at":"2024-03-18 11:49:56","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":74712,"visible":true,"origin":"","legend":"","description":"","filename":"suppltableszenodo.docx","url":"https://assets-eu.researchsquare.com/files/rs-3994211/v1/3a803e5b93bf7fd47ef40af9.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Serum D-serine to total serine ratio and glycine levels as predictive biomarkers for cognitive dysfunction in frail elderly subjects","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFrailty is a complex clinical syndrome characterized by a progressive deterioration of physiological function of multiple organ systems, with consequent increased vulnerability to stressors and adverse health outcomes\u003csup\u003e1\u003c/sup\u003e. Frailty is common in elderly populations, with a prevalence in high-income countries ranging from 4 to 16 percent in people over 65 years of age and featuring a two-fold higher risk in women than men\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrailty is recognized as a main determinant of disability, institutionalization and mortality among older people. However, frailty also represents a dynamic condition which exists on a continuum from fit to frail, where a subject\u0026rsquo;s status can change in either direction over time\u003csup\u003e5\u003c/sup\u003e. Previous longitudinal studies showed indeed that up to 57% of individuals experience at least one transition, which includes both worsening and improvement in frailty state\u003csup\u003e4,6\u003c/sup\u003e. This evidence suggests that the factors concurring to determine frailty may be targeted with preventive interventions to reduce its burden on health outcomes.\u003c/p\u003e \u003cp\u003eHeterogeneous frailty definitions and operational scales have been proposed, with large variations in their biological rationale and included components\u003csup\u003e7\u003c/sup\u003e. Among the most commonly adopted, the Fried\u0026rsquo;s frailty phenotype considers frailty as a biological syndrome and classifies individuals on the basis of five physical components\u003csup\u003e8\u003c/sup\u003e. A few years later, Rolfson and colleagues proposed the Edmonton Frail Scale (EFS), a brief and point-of-care frailty evaluation tool whose reliability is comparable to the most comprehensive geriatric assessment scales\u003csup\u003e9,10\u003c/sup\u003e. Among its nine items, the EFS includes an assessment of primary brain-related functions including cognition, mood and social support, whose impairment represents a key component of frailty and is associated with increased social isolation, disability and mortality\u003csup\u003e11,12\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the physiopathological mechanisms responsible for frailty still remain elusive, frailty prevalence and incidence have been linked to several defective physiological processes, including altered insulin resistance, alterations in energy-regulatory hormones, impaired musculoskeletal system function and mitochondrial energy production, autonomic nervous system dysfunction and systemic inflammation\u003csup\u003e13\u003c/sup\u003e. Consistent with the complex nature of frailty syndrome, several alterations involving multiple pathways and cellular processes in distinct organs have been disclosed by OMICS approaches\u003csup\u003e14\u003c/sup\u003e. In particular, recent metabolomics studies described biochemical alterations in frail subjects, including variations in anti-oxidant, inflammation, purine, urea cycle, kidney markers, tricarboxylic acid cycle and amino acids pathways\u003csup\u003e15,16,25,17\u0026ndash;24\u003c/sup\u003e. The discovery of reliable biomarkers of frailty represents therefore a key milestone for identifying and monitoring the course of this syndrome along aging and, in turn, offering a possible therapeutic approach aimed at reverting frailty. However, previous OMICS results are inconsistent among independent studies\u003csup\u003e14\u003c/sup\u003e and, except for pro-inflammatory soluble cytokines, which are commonly increased in older frail subjects\u003csup\u003e26\u003c/sup\u003e, a unified biochemical marker representative of this syndrome is currently lacking. Moreover, given the critical relevance of cognitive decline, and mood alterations reported in frailty\u003csup\u003e12,27,28\u003c/sup\u003e, the identification of a specific biochemical hallmark mirroring the progressive decay of brain functions before the occurrence of overt dementia represents an unmet clinical need.\u003c/p\u003e \u003cp\u003eIn light of this knowledge gap, here we measured by High Performance Liquid Chromatography (HPLC) a pool of amino acids that collectively are known to modulate glutamatergic receptors activation (L-glutamate, L-aspartate, glycine, D-serine) or to represent the immediate precursors of these neuroactive molecules (L-glutamine, L-asparagine and L-serine) in a well-characterized cohort of elderly subjects encompassing the entire continuum from non-frail to frail condition. Noteworthy, in addition to their neuroactive role, these amino acids play critical roles in regulating various cellular pathways, including protein synthesis, tricarboxylic acid cycle, redox homeostasis, ammonium recycling, purine nucleotide cycle, folate and methionine cycles, and the synthesis of sphingolipids and phospholipids\u003csup\u003e29\u003c/sup\u003e. Consistently with their vital relevance in orchestrating cognitive energy homeostasis and immune system functions, as well as the metabolism of various peripheral organs, such as muscles, liver and kidney, which are severely affected in frail subjects\u003csup\u003e1\u003c/sup\u003e, we investigated the relationship between the serum levels of these metabolites and frailty. We assessed frailty status with (i) the EFS score, which we adopted as a reliable instrument mirroring the multidimensionality of frailty\u003csup\u003e9\u003c/sup\u003e; (ii) the Fried\u0026rsquo;s phenotype, as a well-established tool to evaluate the physical domain of frailty\u003csup\u003e8\u003c/sup\u003e. We also took into account the effect of several comorbidities and health parameters representing key components of frailty and potentially impacting the blood levels of amino acids, including body mass index (BMI), visceral adipose tissue (VAT), sarcopenia, diabetes mellitus and cigarette smoking.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eEnrolment and inclusion/exclusion criteria\u003c/h2\u003e \u003cp\u003eForty-five consecutive hospitalized subjects were recruited at the Physical Medicine and Rehabilitation Unit of Istituto Santa Margherita, Pavia, Italy, between February 2019 and August 2021. Eighty additional outpatients were recruited at the Endocrinology and Nutrition Unit of the same institute. The patients were included if (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) admitted for functional loss secondary to a non-disabling disease; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) aged 65 years or older. The following exclusion criteria were applied: 1) any disease that could directly affect muscle strength (including neurological diseases, hip fractures or amputations); 2) dementia according to DSM-5 criteria\u003csup\u003e30\u003c/sup\u003e; 3) any systemic condition potentially affecting serum amino acid levels, including kidney, liver, rheumatologic and neoplastic diseases, history of drug or alcohol abuse; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) history of altered serum creatinine levels (\u0026gt;\u0026thinsp;1.2 mg/dl) or liver function parameters (aspartate transaminase or alanine transaminase\u0026thinsp;\u0026gt;\u0026thinsp;50 U/l).\u003c/p\u003e \u003cp\u003eSmoking status (current/former/never smoker) was assessed trough interview. The total number of drugs habitually taken by subjects was retrieved from medical records. This study was approved by the local ethics committee (protocol 20180097520, 09/11/2018) and was in conformity with the Helsinki Declaration. Written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCognitive and mood evaluation\u003c/h2\u003e \u003cp\u003eEach subject underwent a standardized examination including evaluation of global cognition, performed through the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)\u003csup\u003e31\u003c/sup\u003e, and of depressive symptoms, measured with the Hamilton Depression Rating Scale (HAM-D)\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuality of life\u003c/h2\u003e \u003cp\u003eQuality of life was assessed through the Italian validation of the 36-Item Short Form Survey (SF-36)\u003csup\u003e33\u003c/sup\u003e. The arithmetic mean of the scores obtained in the nine scales of SF-36 was used as a global measure to compare the quality of life between non-frail and frail groups. We used the General Health scale score of SF-36 as a single frailty domain to be correlated with serum amino acids levels, since it is the SF-36 scale semantically closer to the General Health Status item of the EFS\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSarcopenia and visceral adiposity\u003c/h2\u003e \u003cp\u003eBody composition (fat mass (FM) and fat-free mass (FFM)) was evaluated using fan-beam dual‐energy X‐ray absorptiometry (DXA) (Lunar Prodigy DXA, GE Medical Systems). The \u003cem\u003ein vivo\u003c/em\u003e coefficients of variation were 0.89% and 0.48% for FM and FFM, respectively. Skeletal Muscle Index (SMI) was calculated as the sum of fat-free soft tissue mass of arms and legs divided for height squared\u003csup\u003e34\u003c/sup\u003e. Visceral adipose tissue (VAT) volume was estimated using a constant correction factor (0.94 g/cm\u003csup\u003e3\u003c/sup\u003e). The software automatically placed a quadrilateral box, representing the android region, outlined by the iliac crest and with a superior height equivalent to 20% of the distance from the top of the iliac crest to the base of the skull\u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional performance and independence\u003c/h2\u003e \u003cp\u003eHandgrip strength test was performed using a Jamar dynamometer adhering to the standardized protocol recommended by the American Society of Hand Therapists\u003csup\u003e36\u003c/sup\u003e. Handgrip measurement was assessed on the dominant hand and was considered \u0026ldquo;strong\u0026rdquo; or \u0026ldquo;weak\u0026rdquo; based on sex and body mass index (BMI)-adjusted cut-off scores, as previously described\u003csup\u003e8\u003c/sup\u003e. Basic Activities of Daily Living (BADL) and Independent Activities of Daily Living (IADL) were measured by interviewing the patients and caregivers\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNutritional status\u003c/h2\u003e \u003cp\u003eNutritional status was evaluated with Mini Nutritional Assessment (MNA), which is composed of 18 items divided in four categories: anthropometric assessment, general state, dietary assessment and self-assessment. A score\u0026thinsp;\u0026ge;\u0026thinsp;24 points indicates a good nutritional status; a score between 17 and 23.5 points indicates risk of malnutrition, while a score\u0026thinsp;\u0026le;\u0026thinsp;17 points indicates malnutrition\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFrailty\u003c/h2\u003e \u003cp\u003eFrailty was separately evaluated with the EFS and the frailty phenotype. The EFS assesses nine frailty domains frailty (cognition, general health, functional independence, social support, medication usage, nutrition, mood, continence, functional performance)\u003csup\u003e9\u003c/sup\u003e. EFS score ranges from 0 to 17. Participants were classified as \u0026ldquo;non-frail\u0026rdquo; (EFS\u0026thinsp;\u0026le;\u0026thinsp;5) or \u0026ldquo;frail\u0026rdquo; (EFS\u0026thinsp;\u0026gt;\u0026thinsp;5) according to previously proposed cut-off\u003csup\u003e10\u003c/sup\u003e. Since only three subjects had an EFS score\u0026thinsp;\u0026gt;\u0026thinsp;11 (used to define the \u0026ldquo;severe frail\u0026rdquo; category\u003csup\u003e10\u003c/sup\u003e), we considered all the subjects with an EFS score\u0026thinsp;\u0026gt;\u0026thinsp;5 as a single \u0026ldquo;frail\u0026rdquo; group.\u003c/p\u003e \u003cp\u003eThe physical frailty phenotype contains 5 criteria, including weight loss, exhaustion, low physical activity, slow walking speed and low grip strength\u003csup\u003e8\u003c/sup\u003e. Participants who met 3 or more criteria were defined \u0026ldquo;frail\u0026rdquo;, those who met 1 or 2 criteria were classified as \u0026ldquo;pre-frail\u0026rdquo; and those who met no criteria were defined \u0026ldquo;non-frail\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCollection and storage of serum samples\u003c/h2\u003e \u003cp\u003eBlood sampling was performed after a 12-hour fasting. Whole blood was collected by peripheral venipuncture into clot activator tubes and gently mixed. Sample was stored upright for 30 min at room temperature to allow blood to clot, and centrifuged at 2000 \u0026times; g for 10 min at room temperature. Serum was aliquoted (0.5 ml) in polypropylene cryotubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C before usage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eHPLC analysis of amino acids content\u003c/h2\u003e \u003cp\u003eSerum samples (100 \u0026micro;l) were mixed in a 1:10 dilution with HPLC-grade methanol (900 \u0026micro;l) and centrifuged at 13,000xg for 10 min; supernatants were dried and then suspended in 0.2 M trichloroacetic acid (TCA). TCA supernatants were then neutralized with 0.2 M NaOH and subjected to precolumn derivatization with o-phthaldialdehyde /N-acetyl-L-cysteine in 50% methanol. Amino acids derivatives were resolved on a UHPLC Nexera X3 system (Shimadzu) by using a Shim-pack GIST C18 3-\u0026micro;m reversed-phase column (Shimadzu, 4.0x150 mm) under isocratic conditions (0.1 M sodium acetate buffer, pH 6.2, 1% tetrahydrofuran, and 1 ml/min flow rate). A washing step in 0.1 M sodium acetate buffer, 3% tetrahydrofuran and 47% acetonitrile, was performed after every run. Identification and quantification of amino acids were based on retention times and peak areas, compared with those associated with external standards. The detected amino acids concentration was expressed as \u0026micro;M.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eClinical and demographic characteristics were described using, as summary statistics, median and the interquartile range (IQR) or absolute and relative frequencies. The comparison of clinical-demographic features between non-frail and frail groups were performed with Mann-Whitney U test (for binary EFS-based stratification) or Kruskal-Wallis test (for the three frailty-phenotype categories) for continuous variables and Chi-square test for categorical variables. The normality of data distribution was checked with the Kolmogorov\u0026ndash;Smirnov test. Due to non-normal distribution, the serum amino acid levels were log\u003csub\u003e10\u003c/sub\u003e-transformed and then compared between frail and non-frail groups using a four-way ANCOVA model with \u0026ldquo;frailty status\u0026rdquo;, \u0026ldquo;sex\u0026rdquo;, \u0026ldquo;type 2 diabetes\u0026rdquo; and \u0026ldquo;smoking\u0026rdquo; as factors and \u0026ldquo;age\u0026rdquo; and \u0026ldquo;BMI\u0026rdquo; as covariates. Levene\u0026rsquo;s test was used to check the equality of variances between groups.\u003c/p\u003e \u003cp\u003eThe correlation of serum amino acid concentration with age was evaluated with Spearman\u0026rsquo;s correlation test. Partial correlation analyses adjusted for the effect of age and sex were adopted to test the correlation between serum amino acid levels and EFS score and the other clinical variables. To assess the ability of serum amino acids levels to predict EFS score, we used multiple linear regression models including age, sex, the clinical predictors of EFS score\u003csup\u003e10\u003c/sup\u003e and the single amino acid concentrations as predictors and EFS score as dependent variable. To evaluate the ability of serum amino acids to predict the physical frailty phenotype\u003csup\u003e8\u003c/sup\u003e we adopted multinomial logistic regression models using age, sex and the single amino acid concentration as predictors and frailty category as dependent variable. For linear regression analyses, we verified that the residuals were normally distributed, there was no heteroscedasticity and no multicollinearity between the variables (variance inflation factor\u0026thinsp;\u0026lt;\u0026thinsp;5). The latter was also evaluated in the logistic regression analyses. Significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses. Data were analysed by using SPSS 26.0 software (IBM, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eOne-hundred and twenty-five consecutive elderly subjects were enrolled in the study. The participants were stratified in non-frail (n\u0026thinsp;=\u0026thinsp;74) and frail (n\u0026thinsp;=\u0026thinsp;51) groups accordingly to EFS score\u003csup\u003e10\u003c/sup\u003e. Demographic and clinical features of study participants are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Frail subjects were older and showed higher females prevalence than non-frail participants. As expected, frail group showed worse performance in physical, sarcopenia, cognitive, nutritional, functional independence, and quality of life domains. Total medication count was higher in frail compared to non-frail group. The proportions of patients with type 2 diabetes mellitus and of current/former/never smokers was similar between non-frail and frail subjects.\u003c/p\u003e \u003cp\u003eMMSE and MoCA scores did not correlate with age in either non-frail (r\u0026thinsp;=\u0026thinsp;0.214, p\u0026thinsp;=\u0026thinsp;0.075 and r = -0.013, p\u0026thinsp;=\u0026thinsp;0.918, respectively) or frail group (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.180, p\u0026thinsp;=\u0026thinsp;0.220 and r = -0.236, p\u0026thinsp;=\u0026thinsp;0.106, respectively), indicating that the difference in MoCA score between non-frail and frail groups was not attributable to the older age of frail subjects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSerum levels of D-serine and D-/Total serine ratio correlate with EFS score\u003c/h2\u003e \u003cp\u003eWe first investigated whether the serum levels of amino acids were different between frail and non-frail groups adjusting for the effect of the potential confounders. ANCOVA showed no between-group differences in D-serine, L-serine or any of the other amino acids level (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo further address this issue, we measured the partial correlation between the quantitative EFS score and the serum concentrations of amino acids, adjusting for age and sex. We found a significant mild positive correlation of EFS with serum D-serine (r\u0026thinsp;=\u0026thinsp;0.197, p\u0026thinsp;=\u0026thinsp;0.032) and D-/Total serine ratio (r\u0026thinsp;=\u0026thinsp;0.213, p\u0026thinsp;=\u0026thinsp;0.020), but not the other amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Suppl. Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of Serum levels of D-serine and D-/Total serine ratio with demographic and clinical features\u003c/h2\u003e \u003cp\u003eWe also investigated whether diabetes, obesity (BMI, VAT), sarcopenia (SMI) and cigarette smoking affected the serum concentration of amino acids. Diabetic subjects showed higher levels of L-asparagine, L-serine, L-glutamate, L-glutamine/L-glutamate ratio than non-diabetic participants (Suppl. Table\u0026nbsp;2). After adjustment for age and sex, L-glutamate and L-Glutamine/L-Glutamate correlated with (i) BMI and VAT in both non-frail and frail participants; (ii) SMI only in the non-frail group (Suppl. Table\u0026nbsp;3). Current and former smokers had reduced L-glutamine/L-glutamate ratio compared to never smokers (Suppl. Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eSerum D-serine correlated with age in the frail (r\u0026thinsp;=\u0026thinsp;0.299, p\u0026thinsp;=\u0026thinsp;0.033) but not in non-frail group, while D-/Total serine ratio correlated with age both in non-frail (r\u0026thinsp;=\u0026thinsp;0.278, p\u0026thinsp;=\u0026thinsp;0.017) and frail subjects (r\u0026thinsp;=\u0026thinsp;0.415, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Suppl. Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSerum levels of D-serine and D-/Total serine ratio are independent predictors of frailty\u003c/h2\u003e \u003cp\u003eFurthermore, to assess whether serum levels of D-serine and D-/Total serine ratio are independently associated with frailty, we performed multiple linear regression models using the quantitative EFS score as dependent variable and the known clinical predictors of EFS\u003csup\u003e10\u003c/sup\u003e, added to the individual amino acids concentrations, as predictors. Interestingly, increased levels of D-serine and D-/Total serine ratio, but not the other amino acids, resulted to be independent predictors of EFS score, along with older age and worse nutritional status, handgrip, global cognition and higher number of drugs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Suppl. Tables B-H). These findings highlight that an abnormally greater serum D-/Total serine ratio, used as an index of D-serine metabolism\u003csup\u003e39\u003c/sup\u003e, along with blood D-serine concentrations, may represent a novel systemic biochemical signature of frailty in elderly people.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(A) Clinical and demographic features of elderly cohort considered as a whole and after stratification by frailty status according to EFS. (B) Serum amino acid levels in elderly cohort considered as a whole and after stratification by frailty status. Data are shown as median (IQR) or absolute frequency (%) for continuous and categorical variables, respectively. The total number of non-frail (NF) and frail (FR) subjects for which data were available is reported in the second column.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eA) Frail vs non-frail: clinical-demographic features\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-frail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.0 (69.5\u0026ndash;81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.0 (68.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.0 (75.0\u0026ndash;85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale sex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (86.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPPB total score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.0 (8.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHandgrip (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0 (16.0\u0026ndash;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0 (20.0-32-0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.0 (12.0\u0026ndash;20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6 (7.1\u0026ndash;8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1 (7.1\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5 (6.9\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 NF, 48 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1 (26.0-27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.2 (26.2\u0026ndash;27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.1 (25.7\u0026ndash;27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.374\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMoCA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 NF, 48 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1 (21.5\u0026ndash;26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3 (23.4\u0026ndash;26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.4 (19.7\u0026ndash;25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 50 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8 (20.6\u0026ndash;25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0 (23.5\u0026ndash;26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.5 (18.5\u0026ndash;23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBADL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 NF, 46 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (6.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (6.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0 (5.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIADL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 NF, 46 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (6.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0 (8.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0 (4.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHAM-D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 NF, 46 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (2.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (2.0-9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0 (2.0\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.797\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSF-36 (mean score)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 NF, 46 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.8 (52.7\u0026ndash;78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.7 (57.4\u0026ndash;81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.9 (38.1\u0026ndash;67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 NF, 49 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (2.5-8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0 (5.0-11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType 2 diabetes, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7 (24.2\u0026ndash;32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9 (24.2\u0026ndash;31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.6 (23.7\u0026ndash;33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.752\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVAT (g)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1035 (548\u0026ndash;1557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1049 (530\u0026ndash;1652)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e960 (555\u0026ndash;1502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.463\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smokers, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.152\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFormer smokers, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNever smokers, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (75.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (62.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (2.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (1.0\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0 (6.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB) Frail vs non-frail: serum amino acid levels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;125)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNon-frail (n\u0026thinsp;=\u0026thinsp;74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFrail (n\u0026thinsp;=\u0026thinsp;51)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL-aspartate (\u0026micro;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0-5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9 (3.0-5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4 (3.1\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL-asparagine (\u0026micro;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1 (19.8\u0026ndash;34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8 (20.8\u0026ndash;28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.6 (19.2\u0026ndash;28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycine (\u0026micro;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208.9 (174.0-288.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201.0 (161.8-268.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e222.3 (185.6-400.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD-serine (\u0026micro;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (1.6\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.5\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1 (1.7\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL-serine (\u0026micro;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.5 (60.0-88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.8 (62.3\u0026ndash;89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.1 (54.5\u0026ndash;85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD-/Total serine (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (2.0-3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3 (1.9\u0026ndash;2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8 (2.2-4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL-glutamate (\u0026micro;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7 (19.1\u0026ndash;34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3 (18.8\u0026ndash;33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3 (19.2\u0026ndash;34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL-glutamine (\u0026micro;M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323.0 (280.5-370.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325.3 (282.0-365.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e316.5 (276.3-381.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL-glutamine/L-glutamate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 NF, 51 FR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1 (9.8\u0026ndash;16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.6 (10.1\u0026ndash;16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2 (9.8\u0026ndash;17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Mann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e Chi Square test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003e Four-way ANCOVA with frailty status, sex, diabetes and smoking as factors, age and BMI as covariates. The analysis was conducted on log-transformed amino acid concentrations to normalize the data distribution. Log-transformed values are reported as Suppl. Table A in Zenodo repository DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.10669703\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.10669703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression models for EFS prediction, including clinical variables and serum D-serine (model 1) or D-/Total serine ratio (model 2) as predictors. Complete clinical data were available for n\u0026thinsp;=\u0026thinsp;110 subjects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModel 1: D-Serine and clinical features as predictors of EFS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandgrip (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAM-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-serine (\u0026micro;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: D-/Total serine and clinical features as predictors of EFS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eStd β\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandgrip (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAM-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-/Total serine (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between the serum levels of amino acids and HAM-D score in the elderly cohort stratified in non-frail and frail groups according to EFS. HAM-D score was available for 72 non-frail and 46 frail participants. Correlation coefficients and p-values refer to age and sex-adjusted partial correlations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eL-aspartate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eL-asparagine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eD-serine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eL-serine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eD-/Total serine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eL-glutamate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eL-glutamine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eL-glutamine/L-glutamate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-frail\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrail\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIncreased serum glycine and D-/Total serine ratio correlate with worse global cognition in frail elderly subjects\u003c/em\u003e \u003c/p\u003e \u003cp\u003eNext, we investigated whether serum D-serine, D-/Total serine ratio and the other amino acids were associated with one or more of the frailty domains which concur to determine the EFS score. Notably, we found negative partial correlations between (i) glycine, D-/Total serine ratio and MMSE; (ii) glycine and MoCA score in the frail but not in the non-frail subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The other amino acids did not correlate with cognitive measures (Suppl. Figure\u0026nbsp;1 and Suppl. Table I). Moreover, L-asparagine and L-glutamine correlated negatively with HAM-D score, while glycine levels increased with worse depressive symptoms in frail but not in non-frail subjects (Table\u0026nbsp;4). There were no significant correlations between the serum amino acids and the other frailty domains (Suppl. Tables J-K).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, these findings indicate that dysregulated blood glycine amount and D-/Total serine ratio may represent a metabolic signature of cognitive impairment and depressive symptoms in frail older subjects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSerum D-serine and D/Total serine do not correlate with physical frailty phenotype\u003c/h2\u003e \u003cp\u003eTo further evaluate the relationship between serum amino acids and frailty, we stratified the elderly cohort according to Fried\u0026rsquo;s frailty phenotype\u003csup\u003e8\u003c/sup\u003e. Based on these criteria, 22 subjects were classified as non-frail, 51 as pre-frail and 52 as frail. After adjusting for the effect of potential confounders, there were no significant differences in the serum concentrations of the tested amino acids between the 3 groups (Suppl. Table\u0026nbsp;6). To better assess whether the serum levels of these metabolites may associate with physical frailty phenotype, we performed multinomial logistic regression models with Fried phenotype as dependent variable and age, sex and the individual amino acids concentrations as predictors. Remarkably, we found that neither the levels of D-serine, nor those of the other amino acids, were associated with physical frailty or pre-frailty status (Suppl. Table\u0026nbsp;7 and Suppl. Table L).\u003c/p\u003e \u003cp\u003eTaken together, these findings suggest that the blood levels of D-serine and D-/Total serine ratio are not associated with the physical domain of frailty in elderly individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eThe correlations between serum D-serine, D-/Total serine ratio and EFS are driven by female sex\u003c/h2\u003e \u003cp\u003eThe demographic and clinical features of the elderly cohort stratified by sex are reported in Suppl. Table\u0026nbsp;8A. Females showed a worse impairment in physical and quality of life domains compared to males. Although the difference was not statistically significant, females also had a higher EFS score than males. The serum concentrations of amino acids were similar between females and males (Suppl. Table\u0026nbsp;8B). D-serine and D/Total serine ratio positively correlated with age in both sexes, while L-serine selectively decreased with older age only in males (Suppl. Table M). Consistent with these HPLC data, we found that the positive correlation between D-serine, D-/Total serine and EFS score observed in the whole cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was mainly driven by female sex (Suppl. Table N). The multiple linear regression models adjusted for the clinical predictors of EFS showed that the levels of D-serine and D-/Total serine, but not the other amino acids, were independent predictors of EFS score in females (β\u0026thinsp;=\u0026thinsp;0.989, p\u0026thinsp;=\u0026thinsp;0.008 and β\u0026thinsp;=\u0026thinsp;0.748, p\u0026thinsp;=\u0026thinsp;0.007, respectively) but not in males (Suppl. Table O).\u003c/p\u003e \u003cp\u003e \u003cem\u003eIncreased serum glycine levels and D-/Total serine ratio correlate with worse global cognition and quality of life in a sex-dependent manner\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe found a negative correlation between D-serine (r = -0.222, p\u0026thinsp;=\u0026thinsp;0.035) and D-/Total serine (r = -0.289, p\u0026thinsp;=\u0026thinsp;0.006) and MMSE score in females but not in males. Moreover, D-/Total serine negatively correlated with MoCA (r = -0.235, p\u0026thinsp;=\u0026thinsp;0.025), SF-36 General Health (r = -0244, p\u0026thinsp;=\u0026thinsp;0.021) and SPPB total score (r = -0.209, p\u0026thinsp;=\u0026thinsp;0.043) in females but not in males. Finally, L-asparagine and L-glutamine correlated negatively with HAM-D score, while glycine increased with worse depressive symptoms in females but not in males (Suppl. Tables P-W).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCompelling studies have shown that changes in the cerebrospinal fluid (CSF) and blood levels of\u003c/p\u003e \u003cp\u003eamino acids acting on the glutamatergic N-methyl-D-aspartate receptor (NMDAR) represent a neurochemical signature in various neuropathologies. These include psychiatric conditions such as schizophrenia\u003csup\u003e39,40\u003c/sup\u003e and major depression\u003csup\u003e41\u003c/sup\u003e, and a wide spectrum of neurological diseases, including Alzheimer\u0026rsquo;s disease (AD)\u003csup\u003e42\u0026ndash;44\u003c/sup\u003e, frontotemporal dementia\u003csup\u003e45\u003c/sup\u003e, Parkinson\u0026rsquo;s disease (PD)\u003csup\u003e46\u0026ndash;49\u003c/sup\u003e, amyotrophic lateral sclerosis\u003csup\u003e50,51\u003c/sup\u003e, mild cognitive impairment\u003csup\u003e52,53\u003c/sup\u003e, multiple sclerosis\u003csup\u003e54,55\u003c/sup\u003e and traumatic brain injury\u003csup\u003e56\u003c/sup\u003e. Surprisingly, no investigation so far specifically addressed the relationship between these neuroactive molecules and frailty phenotypes, including those related to cognitive decline and depression. Here, we sought to fill this gap by investigating the endogenous levels of D-serine, glycine and the other amino acids acting on glutamatergic neurotransmission in a well-characterized cohort of older subjects encompassing the entire continuum existing between fit and frail aging. Overall, our biochemical determinations suggest that disrupted systemic D-serine homeostasis may represent a potential predictive biomarker of frailty, while increased serum glycine and D-/Total serine ratio could be specifically associated with cognitive decline and depression in frail elderly individuals.\u003c/p\u003e \u003cp\u003ePrevious blood metabolomics studies identified several metabolites associated with frailty, belonging to redox homeostasis, inflammation, amino acids, purine metabolism, urea and tricarboxylic acid cycles and sugar metabolism pathways\u003csup\u003e14\u003c/sup\u003e. Among the amino acids identified as dysregulated, glutamate metabolism was found to be affected in frail compared to non-frail subjects\u003csup\u003e16\u0026ndash;18,21,25\u003c/sup\u003e. In light of this finding, and given the close relationship linking frailty with cognitive decline\u003csup\u003e12,27,57\u003c/sup\u003e, we investigated whether the serum levels of amino acids acting on glutamatergic NMDAR and their precursors could predict frailty status, and specifically its cognitive domain, in elderly adults. Interestingly, we found that serum D-serine is an independent predictor of the EFS score. D-serine is synthetized by serine racemase (SR)\u003csup\u003e58\u003c/sup\u003e starting from its L-enantiomer and then degraded through D-amino acid oxidase (DAO) activity\u003csup\u003e59,60\u003c/sup\u003e. Once released in the forebrain, D-serine act as an obligatory co-agonist at the glycine modulatory site on GluN1 subunit of NMDAR, a ionotropic glutamatergic receptor playing a key role in sensorimotor gating, synaptic plasticity and cognitive functions\u003csup\u003e61\u003c/sup\u003e. Despite a few reports suggested that circulating blood D-serine concentration decrease\u003csup\u003e61\u003c/sup\u003e or remains unchanged\u003csup\u003e49,62,63\u003c/sup\u003e during healthy aging, recent studies found a positive correlation between serum D-serine and age in patients affected by AD and PD\u003csup\u003e49,63\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur observations showing that D-serine and D-/Total serine ratio significantly increase with ageing in frail but not in non-frail controls suggests that a dysregulation of blood D-serine homeostasis may represent a common ageing-related metabolic variation across different neuropathologies.\u003c/p\u003e \u003cp\u003eWhile EFS was conceived to evaluate frailty through a multidimensional approach, the Fried\u0026rsquo;s frailty phenotype is a widely used tool able to capture the physical domain of frailty\u003csup\u003e8\u003c/sup\u003e. Notably, we failed to find any association between D-serine or the other amino acids levels and frailty phenotype. Therefore, we argue that D-serine may not mirror all the components of frailty syndrome, but could instead represent a specific biochemical fingerprint of its cognitive domain. Consistent with this view, D-serine levels have recently been proposed as an early gender-related biomarker of AD since their serum concentrations correlated with cognitive deterioration in female patients\u003csup\u003e44,63\u003c/sup\u003e. However, other Authors failed to confirm significant changes of CSF and blood D-Ser levels in the whole AD clinical spectrum\u003csup\u003e42,64\u003c/sup\u003e. Interestingly, a recent clinical-pathological study showed that Aβ and tau brain deposition and frailty have a synergistic impact in determining the onset of dementia\u003csup\u003e57\u003c/sup\u003e. This finding, considered together with (i) the previous studies linking increased D-serine with AD-related pathology and cognitive decline\u003csup\u003e43,65,66\u003c/sup\u003e and (ii) the ability of D-serine to diffuse across the blood-brain barrier\u003csup\u003e67\u003c/sup\u003e, suggest that blood levels of this D-amino acid could be adopted as a metabolic signature to identify older adults at higher risk of conversion to dementia.\u003c/p\u003e \u003cp\u003eNotably, the stratification of our elderly cohort by sex disclosed that the correlation between serum D-serine, EFS score and global cognition was mainly driven by females. In agreement with this view, recent investigations showed increased D-/Total Ser ratio in the human post-mortem hippocampus and serum of AD female patients compared to healthy females\u003csup\u003e66,68\u003c/sup\u003e. Similarly, we recently found a significant increase of serum D-serine in PD female, but not in male patients, compared to healthy controls\u003csup\u003e49\u003c/sup\u003e. These findings suggest that a dysregulation of blood D-serine may reflect the occurrence of different neuropathologies in a sex-dependent manner. Considering the neuroprotective roles played by estrogens and the compelling evidence that estrogens loss after menopause can accelerate the effect of aging on cognitive functions\u003csup\u003e69\u003c/sup\u003e, we speculate that the link between increased systemic D-serine levels and cognitive decline may be mediated, at least in part, by the reduced estrogens levels which characterize females aging. However, further studies on larger elderly cohorts are needed to address this outstanding issue.\u003c/p\u003e \u003cp\u003eWe also found that higher serum glycine concentrations correlated with worse cognitive function and depressive symptoms in the frail but not in the non-frail group. Similarly to D-serine, glycine binds the GluN1 subunit of NMDAR and acts as a major obligatory co-agonist\u003csup\u003e70\u003c/sup\u003e. However, in other central nervous system (CNS) regions, glycine also regulates inhibitory neurotransmission via glycine receptors (GlyR)\u003csup\u003e71\u003c/sup\u003e. Considering that glycinergic transmission has been implicated in the physiopathology of cognitive decline and depression\u003csup\u003e70,71\u003c/sup\u003e, the correlation between blood glycine levels, cognitive performance and depressive symptoms may mirror a dysregulation of this amino acid in the CNS of frail subjects.\u003c/p\u003e \u003cp\u003eDespite our biochemical data are highly intriguing, the findings of the present study should be interpreted cautiously, bearing in mind that (i) the assessment of AD and other dementia-related biomarkers was not included in this study, thus preventing any inference linking the serum amino acid levels and the presence of concomitant neurodegenerative diseases; (ii) frailty is characterized by a decline in the function of multiple organ systems, which may directly influence the serum concentration of D-serine, glycine and the other amino acids. Indeed, recent studies showed that blood D-serine levels correlate positively with biochemical renal parameters\u003csup\u003e62,72\u0026ndash;74\u003c/sup\u003e, while various L-amino acids correlated with metabolic parameters such as liver enzymes, lipids and blood glucose\u003csup\u003e62\u003c/sup\u003e. In the kidney and liver, glycine is rapidly interconverted with L-serine, which may then enter the glycolytic or gluconeogenic pathways through its conversion in pyruvate, or be employed in phospholipids biogenesis or mitochondrial metabolism\u003csup\u003e75\u003c/sup\u003e. Dietary intake and D-amino acids produced by the gut microbiota may also affect serine enantiomers metabolism\u003csup\u003e76,77\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, studies in animal models showed that D-serine is detectable in multiple organs, including heart, pancreas, spleen, liver, kidney, lung and muscles\u003csup\u003e78,79\u003c/sup\u003e, and glutamatergic receptors play relevant functions in the modulation of physiological processes in several peripheral tissues\u003csup\u003e80\u003c/sup\u003e. Of note, recent studies showed that serine racemase and NMDAR are highly expressed in human pancreatic islet β cells\u003csup\u003e81\u003c/sup\u003e, and systemic D-serine administration modulates insulin secretion in a dose-dependent manner\u003csup\u003e82,83\u003c/sup\u003e. Despite we found similar serum D-serine levels between diabetic and non-diabetic subjects, L-serine and L-glutamate were increased in diabetic compared to non-diabetic group, consistently with previous blood metabolomics evidence\u003csup\u003e17,25\u003c/sup\u003e. In line with other studies\u003csup\u003e84\u003c/sup\u003e, we also observed a positive correlation between serum L-glutamate concentration, BMI and visceral adiposity in both non-frail and frail participants. Although the biological mechanisms responsible for this association are still unclear, considering that glutamate signalling modulates the immune system\u003csup\u003e85\u003c/sup\u003e and that increased VAT promotes systemic inflammation\u003csup\u003e86\u003c/sup\u003e, elevated blood L-glutamate levels could represent a metabolic signature underpinning the abnormal increase in oxidative stress and inflammation associated to obesity. Concurrently, our data suggest a correlation between serum L-glutamate concentration and SMI. This is in line with previous investigations showing that glutamate is crucial in maintaining the homeostasis of energy metabolism in skeletal muscle\u003csup\u003e87\u003c/sup\u003e. Surprisingly, this relationship was observed in the non-frail but not in frail group, suggesting that different biological pathways may modulate the maintenance of skeletal muscle mass across healthy and frail aging. However, our results may be affected by the very low prevalence of subjects with SMI scores below the proposed cut-off to define sarcopenia\u003csup\u003e34\u003c/sup\u003e and therefore require validation in larger cohorts.\u003c/p\u003e \u003cp\u003eBesides its neuroactive role, glycine primarily influences anti-oxidative reactions and immune system\u003csup\u003e75\u003c/sup\u003e. In agreement with this knowledge, glycine has been used to prevent tissue injury, enhance anti-oxidative capacity, improve immunity, and treat metabolic disorders in obesity, diabetes and various inflammatory diseases\u003csup\u003e88\u003c/sup\u003e. Thus, consistent with the multiple beneficial effects of glycine, we cannot rule out that the negative correlation between this amino acid and cognitive functions in frail older individuals might represent an epiphenomenon triggered by inflammation and metabolic dysfunctions, rather than being causally linked to memory impairments. In the same way, we cannot rule out that systemic D-serine metabolism variation in frailty may represent a biochemical adaptation to CNS and multi-system deteriorations. In line with this hypothesis, D-serine supplementation or treatment with DAO inhibitors significantly improved cognitive functions in healthy subjects, PD and schizophrenia patients\u003csup\u003e89\u0026ndash;92\u003c/sup\u003e. Future investigations are warranted to clarify these important issues.\u003c/p\u003e \u003cp\u003eThe strengths of our work include (i) the novelty of investigating NMDAR-related amino acids and their precursors in the serum of a well-characterized elderly cohort, including the entire clinical spectrum existing from fit to frail condition; (ii) the correlation of serum amino acids with multiple potential confounding factors, such as diabetes, body composition and cigarette smoking\u003csup\u003e93\u003c/sup\u003e; (iii) the assessment of frailty with two different but complementary tools\u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, we also acknowledge some limitations. First, the cross-sectional design and the clinical-biochemical correlations observed in the present study did not allow to draw any causal relationship between the serum changes in amino acids levels and the clinical phenotypes. Future longitudinal studies on larger elderly cohorts adopting a multidimensional approach, including the measurement of blood biomarkers mirroring brain (e.g. neurofilament light chain, Aβ42/Aβ40 ratio, phosphorylated tau\u003csup\u003e94\u003c/sup\u003e) and peripheral organs damage, as well as inflammation, are warranted to elucidate this issue. Second, the sex ratio was unbalanced with an higher prevalence of females, potentially biasing the analyses conducted after stratifying the cohort by sex. Third, the assessment of biochemical parameters of kidney and liver function was not included in the study protocol, thus preventing the adjustment of the analyses for the serum levels of creatinine, aspartate transaminase and alanine transaminase, which correlate with the blood levels of D-Ser and several L-amino acids, respectively\u003csup\u003e62\u003c/sup\u003e. However, the history of any kidney or liver disease or altered parameters of renal and hepatic function was strictly considered as an exclusion criteria at the time of participants enrolment.\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights serum D-/serine and glycine as putative biochemical signatures of cognitive decline and depression in frail older subjects. The observation that D-serine correlates with frailty scores and global cognition in females but not in males suggest that this effect may also be modulated by sex-related biological factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all the patients, their caregivers and the research subjects involved in this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors declare no competing financial interest related to this manuscript. Other disclosures are reported below.\u003c/p\u003e\n\u003cp\u003eAlberto Imarisio reports no disclosures.\u003c/p\u003e\n\u003cp\u003eIsar \u0026nbsp;Yahyavi reports no disclosures.\u003c/p\u003e\n\u003cp\u003eClara Gasparri reports no disclosures.\u003c/p\u003e\n\u003cp\u003eAmber Hassan reports no disclosures.\u003c/p\u003e\n\u003cp\u003eMicol Avenali is member of the Monogenic Network of ASAP GP2 (Global Parkinson genetic Program); received speaker\u0026apos;s honoraria from Bial Italia and Zambon. She received research funding for research projects from the Italian Ministry of Health.\u003c/p\u003e\n\u003cp\u003eAnna Di Maio reports no disclosures\u003c/p\u003e\n\u003cp\u003eGabriele Buongarzone reports no disclosures.\u003c/p\u003e\n\u003cp\u003eCaterina Galandra reports no disclosures.\u003c/p\u003e\n\u003cp\u003eMarta Picascia reports no disclosures.\u003c/p\u003e\n\u003cp\u003eAsia Filosa reports no disclosures.\u003c/p\u003e\n\u003cp\u003eMaria Cristina Monti reports no disclosures.\u003c/p\u003e\n\u003cp\u003eClaudio Pacchetti reports no disclosures.\u003c/p\u003e\n\u003cp\u003eFrancesco Errico reports no disclosures.\u003c/p\u003e\n\u003cp\u003eMariangela Rondanelli reports no disclosures.\u003c/p\u003e\n\u003cp\u003eAlessandro Usiello reports no disclosures.\u003c/p\u003e\n\u003cp\u003eEnza Maria Valente is Associate Editor of Journal of Medical Genetics; is Genetics Section Editor of Pediatric Research, of The Cerebellum, and of Neurological Sciences; is member of the Editorial Board of Movement Disorders Clinical Practice; is member of the Steering Committee of ASAP GP2 (Global Parkinson genetic Program). E.M.V. received research support from the Italian Ministry of Health, CARIPLO Foundation, Telethon Foundation Italy, Pierfranco and Luisa Mariani Foundation, and European Commission (Eranet Neuron).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by grants from CARIPLO Foundation (grant nr. 2017-0575 to EMV and AU), Italian Ministry of Health (Ricerca Corrente 2023 to IRCCS Mondino Foundation) and Italian Ministry of University and Research (PRIN 2022 - COD. 2022XF7YYL_02 to AU).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe work of E.M.V. and A.U. is supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) \u0026ndash; A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlberto Imarisio: Data curation; Formal analysis; Investigation; Writing - original draft; Writing - review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIsar Yahyavi: Data curation; Investigation; Methodology; Writing - review \u0026amp; editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClara Gasparri: Data curation; Investigation; Methodology; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAmber Hassan:\u0026nbsp;Data curation; Investigation; Methodology; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eMicol Avenali: Data curation; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eAnna Di Maio: Data curation; Investigation; Writing - review \u0026amp; editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGabriele Buongarzone: Data curation; Formal analysis; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eCaterina Galandra: Data curation; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eMarta Picascia: Data curation; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eAsia Filosa: Formal analysis; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eMaria Cristina Monti: Formal analysis; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eClaudio Pacchetti: Data curation; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eFrancesco Errico: Data curation; Investigation; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eMariangela Rondanelli: Data curation; Investigation; Methodology; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eAlessandro Usiello: Conceptualization; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eEnza Maria Valente: Conceptualization; Funding acquisition; Investigation; Project administration; Resources; Supervision; Validation; and Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated during the current study is available in the ZENODO repository (DOI: 10.5281/zenodo.10669703).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoogendijk EO, Afilalo J, Ensrud KE, Kowal P, Onder G, Fried LP. 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Clin Chem Lab Med 2024. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/cclm-2023-1434\u003c/span\u003e\u003cspan address=\"10.1515/cclm-2023-1434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3994211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3994211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFrailty is a common age-related clinical syndrome characterized by a decline in the function of multiple organ systems, increased vulnerability to stressors and huge socio-economic burden. Despite recent research efforts, the physiopathological mechanisms concurring to determine frailty remain elusive and biomarkers able to predate its occurrence in the early stages are still lacking. Beyond its physical component, cognitive decline represents a critical domain of frailty associated with higher risk of adverse health outcomes.\u003c/p\u003e\n\u003cp\u003eWe measured by High Performance Liquid Chromatography (HPLC) a pool of serum amino acids including L-glutamate, L-aspartate, glycine and D-serine, as well as their precursors L-glutamine, L-asparagine and L-serine in a cohort of elderly subjects encompassing the entire continuum from fitness to frailty. These amino acids are known to orchestrate excitatory and inhibitory neurotransmission, and in turn, to play a key role as intermediates of energy homeostasis and in liver, kidney, muscle and immune system metabolism. To comprehensively assess frailty, we employed both the Edmonton Frail Scale (EFS), as a practical tool to capture the multidimensionality of frailty, and the frailty phenotype, as a measure of physical function. We found that D-serine and D-/Total serine ratio were independent predictors of EFS but not of physical frailty. Furthermore, higher glycine levels and D-/Total serine correlated with worse cognition and depressive symptoms in the frail group. These findings suggest that altered homeostasis of D-serine may represent a biochemical signature of frailty, while increased serum glycine and D-/Total serine ratio could be specifically associated with cognitive decline and depression in frail older populations.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e*Alberto Imarisio and Isar Yahyavi share first authorship\u003cbr\u003e\n**Alessandro Usiello and Enza Maria Valente share senior authorship\u003c/p\u003e","manuscriptTitle":"Serum D-serine to total serine ratio and glycine levels as predictive biomarkers for cognitive dysfunction in frail elderly subjects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-18 11:41:51","doi":"10.21203/rs.3.rs-3994211/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-05-14T13:27:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-04-01T20:57:04+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-03-26T00:12:14+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-03-18T07:08:06+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-03-17T00:00:10+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-03-15T03:23:48+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-03-14T21:29:20+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-03-14T17:06:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-29T11:34:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2024-02-28T13:57:09+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-02-28T11:57:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-27T14:56:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e0bbb24-5983-48c6-8d40-649f224f2b02","owner":[],"postedDate":"March 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29459207,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":29459208,"name":"Biological sciences/Neuroscience/Molecular neuroscience"},{"id":29459209,"name":"Biological sciences/Neuroscience/Learning and memory/Long-term memory"},{"id":29459210,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":29459211,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2024-07-10T07:06:53+00:00","versionOfRecord":{"articleIdentity":"rs-3994211","link":"https://doi.org/10.1038/s41398-024-02991-z","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2024-07-09 04:00:00","publishedOnDateReadable":"July 9th, 2024"},"versionCreatedAt":"2024-03-18 11:41:51","video":"","vorDoi":"10.1038/s41398-024-02991-z","vorDoiUrl":"https://doi.org/10.1038/s41398-024-02991-z","workflowStages":[]},"version":"v1","identity":"rs-3994211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3994211","identity":"rs-3994211","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}